{"title":"GEE的快速纯R实现:矩阵包的应用","authors":"Lee S McDaniel, Nicholas C Henderson, P. Rathouz","doi":"10.32614/RJ-2013-017","DOIUrl":null,"url":null,"abstract":"Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, geeM, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the Matrix package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of geeM, which is not much worse than C implementations like geepack and gee on small data sets and faster on large data sets.","PeriodicalId":51285,"journal":{"name":"R Journal","volume":"5 1 1","pages":"181-187"},"PeriodicalIF":2.3000,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"47","resultStr":"{\"title\":\"Fast Pure R Implementation of GEE: Application of the Matrix Package\",\"authors\":\"Lee S McDaniel, Nicholas C Henderson, P. Rathouz\",\"doi\":\"10.32614/RJ-2013-017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, geeM, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the Matrix package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of geeM, which is not much worse than C implementations like geepack and gee on small data sets and faster on large data sets.\",\"PeriodicalId\":51285,\"journal\":{\"name\":\"R Journal\",\"volume\":\"5 1 1\",\"pages\":\"181-187\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2013-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"47\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"R Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.32614/RJ-2013-017\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"R Journal","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.32614/RJ-2013-017","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Fast Pure R Implementation of GEE: Application of the Matrix Package
Generalized estimating equation solvers in R only allow for a few pre-determined options for the link and variance functions. We provide a package, geeM, which is implemented entirely in R and allows for user specified link and variance functions. The sparse matrix representations provided in the Matrix package enable a fast implementation. To gain speed, we make use of analytic inverses of the working correlation when possible and a trick to find quick numeric inverses when an analytic inverse is not available. Through three examples, we demonstrate the speed of geeM, which is not much worse than C implementations like geepack and gee on small data sets and faster on large data sets.
R JournalCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍:
The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R.
The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to:
- put their contribution in context, in particular discuss related R functions or packages;
- explain the motivation for their contribution;
- provide code examples that are reproducible.